> ## Documentation Index > Fetch the complete documentation index at: https://docs.reilabs.org/llms.txt > Use this file to discover all available pages before exploring further. # Discovery > How Adapt-1 can form task-facing structure from experience instead of requiring an authored ontology. Discovery changes where task-specific structure comes from in Adapt-1. Adapt-1 can form task-specific state from admitted evidence without a task-specific training dataset. Earlier Domain configurations could still define part of the learner-facing ontology before the first event arrived. Discovery makes that authored ontology optional. A Domain can keep the public task boundary while Adapt-1 forms useful task-facing structure during a zero-start run or a separate acquisition phase. ```text theme={null} public Domain boundary + authored learner-facing structure can become public Domain boundary + admitted evidence ↓ Discovery forms learner-facing structure ``` The Domain remains. The requirement to author its ontology does not. ## What changes A Domain still gives one instance a stable public scope. It can define event meanings, observable outcomes, permitted actions, lifecycle boundaries, and the structured result the application expects. Discovery lets more of the structure inside that boundary form from admitted evidence. | Public boundary remains | Can form from admitted evidence | | ------------------------------------------------------ | ---------------------------------------------------------- | | Event and outcome meanings | Useful learner input paths | | Target or result type | Predictive combinations of observable fields | | Episode and reset boundaries | Bounded temporal relationships | | Available actions and reward semantics when applicable | State-dependent action values and delayed-credit structure | | Observable before/after event boundary | Transition and causal bindings where supported | | Safety and execution constraints | Predictive rules and other task-local structure | This does not move the environment into Core. The world still produces observations and consequences. The application still exposes the public interaction boundary. Discovery changes how much learner-facing structure must be written before Adapt-1 can begin forming useful state. ## Discovery spans several learning paths Discovery is a structure-source option across learning paths that can form task-facing structure from evidence. | Discovery path | What forms from evidence | | ------------------------ | -------------------------------------------------------------------------------- | | **Transition Discovery** | Executable input projections and compatible before/after causal bindings | | **Structure Discovery** | Outcome-useful fields, combinations, bounded temporal lags, and predictive rules | | **Sequential Discovery** | State-dependent action values and delayed credit across ordered interaction | A Domain can use one path or combine compatible paths. Each path keeps its own target, evidence, attribution, and abstention contract while contributing to the same Domain state under the declared learning lifecycle. A Domain can therefore use **discovered**, **authored**, or **mixed** task-facing structure. Authored configuration remains available when a public surface must be fixed before use. ## Discovery does not choose the learning schedule Fix the complete learning-run boundary before classifying the state. Discovery can operate under several schedules. | Schedule | Discovery behavior | | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Zero-start learning across a run** | The complete run begins with empty task-specific learned state. Discovery forms structure from events inside the run and can retain it across compatible episodes or passes. | | **Zero-start task-local learning** | Each independently redrawn task begins empty, forms structure from local evidence, produces its result, and then resets. | | **Separate acquisition, then frozen use** | Discovery forms structure from acquisition records outside the new run. The acquired state is then used without learner writes. | | **Separate acquisition, then continued adaptation** | Discovery forms the starting state outside the new run, then current outcomes continue revising it. | ```text theme={null} empty task-specific state -> admitted evidence -> Discovery forms usable structure -> later work can use that structure ``` The sequence above can happen before a declared run or inside it. State retained from episode 1 into episode 2 is still state earned inside a zero-start run when the complete run began empty. It is not separately acquired state merely because a later episode can use it. ## Three ways to use a Domain | Mode | When to use it | | --------------------- | ----------------------------------------------------------------------------------------------- | | **Discovery** | The public task boundary is clear, but useful task-facing structure should form from experience | | **Authored ontology** | Task-facing structure must be explicit, reviewed, or fixed before use | | **Mixed** | A small reviewed surface should remain fixed while other supported structure is discovered | Authored ontology is therefore still available. Discovery removes its status as a prerequisite. A practical workflow can begin with Discovery, inspect what became usable, evaluate it on held-out work, and later pin reviewed parts of the ontology when a stable production contract is useful. ## Inspectability remains part of the contract Discovery does not turn the Domain into an opaque setup step. Public API responses can expose whether Discovery is still accumulating or ready, which input paths became active, the current learned structure, supporting evidence, contradicting evidence where applicable, learner versions, and abstention states. The exact diagnostics depend on the configured learning relationship. The important property is that discovered structure can be inspected and evaluated before an application relies on it. ## What Discovery does not remove Discovery does not remove the need for a valid task boundary. An application still has to expose the information that exists in the real workflow. It cannot learn from an outcome that is never returned. It cannot use an action the application does not permit. It should not receive a hidden answer, private evaluator state, or post-outcome value as if that information were available before the result. These are interface and evaluation boundaries, not authored task knowledge. ## Why it matters The architectural progression is simple: ```text theme={null} Adapt-1 Preview task-specific state can form from evidence + some learner-facing ontology can be supplied explicitly Discovery the same acquisition and update schedules remain available + authored learner-facing ontology becomes optional + task-facing structure can form from admitted evidence ``` This reduces the amount of task-specific engineering required before an unfamiliar structured environment can become learnable, while preserving an explicit public boundary and inspectable state. Distinguish zero-start learning from state formed in a separate acquisition phase. Configure Discovery, send events, inspect readiness, and query discovered structure. Form state-dependent action values and delayed credit from ordered interaction. Use explicit reviewed task-facing structure when the application requires it. Choose the learning relationship from the observable task loop. See how Discovery fits the wider adaptive substrate.